Machine Learning-Driven Analysis of Soil Nutrients and Environmental Factors for Optimized Crop Selection in Sustainable Agriculture

Gayathri Karanam; Sankalp Paliwal; Naga Avishitha Raparthi; V. Yaswanth Kumar; Arif Shaik

doi:10.2991/978-94-6463-718-2_69

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Machine Learning-Driven Analysis of Soil Nutrients and Environmental Factors for Optimized Crop Selection in Sustainable Agriculture

Authors

Gayathri Karanam¹^{, *}, Sankalp Paliwal¹, Naga Avishitha Raparthi¹, V. Yaswanth Kumar¹, Arif Shaik¹

¹Department of Electronics and Instrumentation Engineering, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India

^*Corresponding author. Email: gayathrichowdary2003@gmail.com

Corresponding Author

Gayathri Karanam

Available Online 23 May 2025.

DOI: 10.2991/978-94-6463-718-2_69 How to use a DOI?
Keywords: ANN; Random Forests; Decision Trees; KNN; Crop Suggestion; NPK; OneM2M
Abstract: The agricultural sector is challenged to optimize crop selection to maximize yield, minimize risk, and enhance profitability. Crop suggestion is a complex problem and involves accurately selecting the most suitable crops for cultivation in a given environment based on many diverse factors such as soil quality, climate, market demand, and resource availability to achieve better yields and profitability while reducing fertilizer abuse over the soil. The crop suggestion is a broad-distributed application that provides numerous benefits for farmers concerning using resources more optimally and reducing the effects of crop failure with this research paper. Are there possibilities by which we can use high-performance computers, data analytics, and machines to make better crop recommendations quickly and efficiently? A comprehensive analysis of this paper will help as a basis for publication and guides that can be followed for better agricultural output.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series: Advances in Computer Science Research
Publication Date: 23 May 2025
ISBN: 978-94-6463-718-2
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-718-2_69 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Gayathri Karanam
AU  - Sankalp Paliwal
AU  - Naga Avishitha Raparthi
AU  - V. Yaswanth Kumar
AU  - Arif Shaik
PY  - 2025
DA  - 2025/05/23
TI  - Machine Learning-Driven Analysis of Soil Nutrients and Environmental Factors for Optimized Crop Selection in Sustainable Agriculture
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 808
EP  - 816
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_69
DO  - 10.2991/978-94-6463-718-2_69
ID  - Karanam2025
ER  -

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